System for targeted display of content

ABSTRACT

Systems and methods relate to targeted display of media content, including a device that includes a memory having program instructions stored therein; and at least one processor configured to execute the program instructions to control the device to: connect via a wide area network to a remote system, receive a rule set or an artificial intelligence (AI) model from the remote system, select media content from a plurality of media content based on applying demographics data to the rule set or AI model, and cause, based on an output of the rule set or AI model, a display, that is local to the device, to display the media content.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Application No.62/791,089, filed on Jan. 11, 2019, and U.S. Provisional Application No.62/823,329, filed on Mar. 25, 2019, the disclosures of which areincorporated herein by reference in their entirety.

FIELD

This disclosure is related to the targeted display of media content, andmore particularly the targeted display of media content using a rule setor an AI model.

BACKGROUND

Some prior systems attempt to provide targeted media content to viewers.However, there is complexity associated with the delivery of highlyrelevant, targeted media content. For example, there is a problem inmanaging and understanding the data that is required to determine whichmedia content should be delivered to which individuals at what time andplace. Moreover, there is a need to better understand the performanceand productivity of entities that are displaying targeted media content,how various factors affect the performance and productivity, and how theperformance and productivity can be enhanced.

SUMMARY

Some embodiments of the present disclosure solve the previouslymentioned problems and other problems of background art.

Some embodiments of the present disclosure enable the targeted displayof media content and the creation of new media content for targeteddisplay. Some embodiments of the present disclosure enable the upload ofdata to be used in analysis to perform targeted display and mediacontent creation, to be used to update rules or an AI model that isdownloadable and used to perform targeted display of media content, andto be used in be analysis, visualization, and reporting.

In some embodiments, a device is provided. The device comprises a memoryhaving program instructions stored therein; and at least one processorconfigured to execute the program instructions to control the device to:connect via a wide area network to a remote system, receive a rule setor an artificial intelligence (AI) model from the remote system, selectmedia content from a plurality of media content based on applyingdemographics data to the rule set or AI model, and cause, based on anoutput of the rule set or AI model, a display, that is local to thedevice, to display the media content.

In an embodiment, the at least one processor is further configured toobtain customer demographics data as the demographic data based onsensor data from at least one sensor, and apply the customerdemographics data to the rule set or AI model. In an embodiment, the atleast one processor is further configured to obtain data from at leastone from among a mobile sensor, a radio frequency identification device,and a beacon.

In an embodiment, the at least one processor is configured to send, tothe remote system, the customer demographics data. In an embodiment, theat least one processor is configured to send, to the remote system, onlya portion of the customer demographics data that is obtained by the atleast one processor, such that an amount of the customer demographicsdata sent to the remote system by the at least one processor is lessthan a total amount of the customer demographics data obtained by the atleast one processor. In an embodiment, the at least one processor isfurther configured to receive, from the remote system, external dataincluding at least one from among event data, weather data, and point ofsale (POS) data, and select the media content from the plurality ofmedia content based on applying the demographics data and the externaldata to the rule set or AI model. In an embodiment, the at least oneprocessor is configured to receive, from the remote system, an updatedrule of the rule set, an updated rule set, or an updated AI model, thatis updated based on the customer demographics data sent to the remotesystem, and is further configured to cause the media content from theplurality of media content to be displayed on the display, based on theupdated rule, the updated rule set, or the updated AI model.

In an embodiment, the plurality of media content is stored in memorythat is external to the device, and the at least one processor isconfigured to cause, based on the output of the rule set or AI model,the display to display the media content from the plurality of mediacontent that is stored in the memory that is external to the device.

In some embodiments, a system is provided. The system comprises thedevice; and the at least one sensor, wherein the at least one sensorincludes a first camera and a second camera, the first camera is locatedin a vicinity of the display, and the second camera is located in aposition, away from the display.

In some embodiments, a system is provided. The system comprises thedevice; and a content management system (CMS) connected by a localnetwork with the device, wherein the device is configured to receive therule set or AI model from the remote system, select the media contentfrom the plurality of media content based on applying the demographicdata to the rule set or AI model, and send, based on the output of therule set or AI model, a signal to the CMS to display the media contentfrom the plurality of media content, and the CMS comprises at least oneprocessor and a memory, and is configured to receive the plurality ofmedia content from the remote system, store the plurality of mediacontent in the memory of the CMS, and cause, based on the signal fromthe ALP appliance, the display to display the media content.

In an embodiment, the CMS is configured to send a list of the pluralityof media content stored by the CMS to the device, and the device isconfigured to send the signal to the CMS to display the media contentfrom the plurality of media content, based on the list of the pluralityof media content being previously received by the device.

In an embodiment, the device is configured to send a plurality ofsignals, including the signal, to the CMS to trigger the CMS to displayone or more of the plurality of media content on the display, each ofthe plurality of signals sent at a different time, and the CMS isconfigured to receive the plurality of signals, while playing mediacontent of a playlist on the display, and further configured to: ignoreeach of the plurality of signals received prior to a first predeterminedtime of the media content of the playlist played, in a case where one ofthe plurality of signals is received at a second predetermined time ofthe media content of the playlist played, later than the firstpredetermined time, play one of the plurality of media content based onthe one of the plurality of signals received, in a case where none ofthe plurality of signals is received at the second predetermined timeand the one of the plurality of signals is received at the firstpredetermined time, play the one of the plurality of media content basedon the one of the plurality of signals received, and in a case wherenone of the plurality of signals is received at the first predeterminedtime and the second predetermined time, play other media content of theplaylist based on a position of the other media content within theplaylist.

In some embodiments, a system is provided. The system comprises a devicecomprising at least one processor and a memory; and a remote systemconfigured to communicate with the device via a wide area network,wherein the remote system is configured to create a rule set or AI modeland send the rule set or AI model via the wide area network to thedevice, the rule set or AI model configured for selecting media contentamong a plurality of media content for displaying on a display, that islocal to the device, and the device is configured to select the mediacontent from the plurality of media content based on applyingdemographics data to the rule set or AI model, and cause, based on anoutput of the rule set or AI model, the display to display the mediacontent.

In an embodiment, the remote system is further configured to receive atleast a portion of the demographics data obtained by the device, andupdate the rule set or AI model sent to the device based on the portionof demographics data received by the remote system from the device. Inan embodiment, the remote system is further configured receive externaldata including at least one from among event data, weather data, andpoint of sale (POS) data, and send the external data to the device, andthe device is configured to select the media content based on applyingthe demographics data and the external data to the rule set or AI model.In an embodiment, the remote system is further configured to create newmedia content based on the portion of demographics data received by theremote system from the device, and send the new media content to acontent management system (CMS), that is connected locally with thedevice and the display, to be stored in memory of the CMS device as apart of the plurality of media content. In an embodiment, the remotesystem is further configured to create customer insights for display ona user display, based on the portion of demographics data received bythe remote system from the device. In an embodiment, the remote systemis configured to receive a proof of delivery (PoD) notification from acontent management system (CMS), that is connected locally with thedevice and the display, and send the rule set or AI model via the widearea network to the device based on previously receiving the PoDnotification.

In some embodiments, a method is provided. The method is performed by adevice comprising at least one processor and a memory, the deviceconnected via a wide area network to a remote system. The methodcomprises receiving a rule set or artificial intelligence (AI) modelfrom the remote system; selecting media content from a plurality ofmedia content based on applying demographics data to the rule set or AImodel; and causing, based on an output of the rule set or AI model, adisplay, that is local to the device, to display the media content.

In an embodiment, the method further comprises obtaining thedemographics data based on sensor data from at least one sensor that isconnected locally with the at least one processor of the device. In anembodiment, the method further comprises uploading, to the remotesystem, at least a portion of the demographics data obtained by thedevice. In an embodiment, the method further comprises downloading, fromthe remote system, external data including at least one from among eventdata, weather data, and point of sale (POS) data, wherein the selectingcomprises selecting the media content from the plurality of mediacontent based on applying the demographics data and the external data tothe rule set or AI model.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 illustrates an overview of a system of an embodiment.

FIG. 2 illustrates a remote system and a local system of an embodiment.

FIG. 3 is a diagram illustrating example functions of an ALP cloud of anembodiment.

FIG. 4 is a diagram illustrating an ALP system of an embodiment andconnected components.

FIG. 5 is a diagram illustrating examples functions of an ALP applianceof an embodiment.

FIG. 6 is a diagram illustrating a CMS of an embodiment and connectedcomponents.

FIG. 7 is a diagram illustrating a machine learning process flow of anembodiment.

FIG. 8 is a diagram illustrating a method of the present disclosure.

FIG. 9 is a diagram illustrating a method of the present disclosure.

FIG. 10A is a diagram demonstrating a first part of an example sequenceof interactions between an ALP system and a CMS of an embodiment.

FIG. 10B is a diagram demonstrating a second part of the examplesequence of interactions between the ALP system and the CMS of theembodiment.

FIG. 10C is a diagram demonstrating a third part of the example sequenceof interactions between the ALP system and the CMS of the embodiment.

FIG. 10D is a diagram demonstrating a fourth part of the examplesequence of interactions between the ALP system and the CMS of theembodiment.

FIG. 10E is a diagram demonstrating a fifth part of the example sequenceof interactions between the ALP system and the CMS of the embodiment.

DETAILED DESCRIPTION

FIG. 1 illustrates a system of an embodiment of the present disclosure.As illustrated in FIG. 1 , the system comprises one or more localsystems 120, a remote system 130, and one or more user terminals 140that may be, for example, computing devices with displays. In anembodiment, each of the local systems 120 may be located in a respectivelocation 110 (e.g. a store). In an embodiment, as illustrated in FIG. 2, each local system 120 may comprise an analytics learning platform(ALP) system 210, a content management system (CMS) 240, devices 290,and displays 280 that are digital displays. The remote system 130 mayinclude, for example, an ALP cloud 310 and a CMS cloud 340.

The local system 120 and the remote system 130 may be in remotecommunication with the each other. For example, with reference to FIG. 2, the ALP cloud 310 of the remote system 130 may be in remotecommunication with the ALP system 210 and the CMS 240 of the localsystem 120, and the CMS cloud 340 of the remote system 130 may be inremote communication with the CMS 240 of the local system 120. The ALPsystem 210, CMS 240, devices 290, and displays 280 of the local system120 may be in local communication with each other. For example, withreference to FIG. 2 , the devices 290 may locally communicate with theALP system 210, the ALP system 210 and the CMS 240 may locallycommunicate with each other, and the CMS 240 may further locallycommunicate with the displays 280. With reference to FIG. 2 , the ALPcloud 310 and the CMS cloud 340 of the remote system 130 may be inremote communication with each other. Remote communication refers to,for example, any wide area network (e.g. the internet). Localcommunication refers to communication using local area networktechnologies without using remote communication. For example, localcommunication includes local area wired connections (e.g. coaxial,twisted pair, fiber optic, etc.) and local area wireless communicationmethods (e.g. Bluetooth, Wi-FI, Zigbee, etc.)

In embodiments, the ALP cloud 310 and the CMS cloud 340 may each be arespective cloud computing system implemented as a cloud server. Inembodiments, the ALP cloud 310 and the CMS cloud 340 may be integratedtogether as a single cloud of the remote system 130, or have theirfunctions divided between any number of separate cloud computing systemsthat may be included in the remote system 130. In embodiments, the ALPcloud 310 and the CMS cloud may alternatively be implemented outside acloud computing system by, for example, a dedicated server(s).

With reference to FIG. 2 , components of the remote system 130 (e.g. theALP cloud 310 and/or the CMS cloud 340) may be configured to perform anynumber of the following example functions in embodiments: (1) receivedata from external sources concerning weather and events; (2) receivedata from the ALP system 210 concerning store count, crowd count, anddemographics data; (3) receive data from retail sources or the ALPsystem 210 concerning point of sales (POS) and customer relationshipmanagement (CRM), inventory, and product catalog; (4) receive data fromthe ALP system 210 or the CMS 240, concerning proof that media contentis played by at least one of the displays 280; (5) receive dataconcerning proof that media content has been sent to the CMS 240 fromthe remote system 130; (6) create and update a rule set or artificialintelligence (AI) model based on the data received from the externalsources, the ALP system 210, and the retail sources, or based on aninput from a user terminal(s) 140; (7) send the rule set or the AI modelto the ALP system 210; (8) create new media content automatically byapplying the data received from the ALP system 210, the externalsources, and the retail sources to a same or different rule set or AImodel stored in the ALP cloud 310, or create the new media contentmanually via an input on a user terminal(s) 140, the new media contentincluding images, video, and/or text that is stored in memory of theremote system 130; (9) update the images, video, and/or text availablein the memory of the remote system 130, based on an input on the userterminal(s) 140; (10) send the new media content to the CMS 240; (11)cause the CMS device to play the new media content; (12) send CMSplaylist data to the local system 120, (13) send the plurality of mediacontent, including the new media content, to the ALP system 210 or theCMS 240; (14) prepare and output a report to a user terminal(s) 140,based on the data received from the CMS 240, the ALP system 210, and theexternal sources, to the user terminal(s) 140; (15) report, on the userterminal(s) 140, the demographics information of the people who havewatched the triggered media content for at least a specified time periodon at least one of the displays 280; (16) report, on the userterminal(s) 140, the time people spent watching the triggered mediacontent on at least one of the displays 280; (17) report, on the userterminal, how many times people watched the triggered media content forat least a specified time period on at least one of the displays 280;and (18) report the time people spent in certain areas covered bysensors connected to the ALP system 120.

In an embodiment, the ALP cloud 310 creates rules or an AI model, andfurther creates media content according to a user's manual instructionsand/or Artificial Intelligence (AI) assistance. The ALP cloud 310 maysend the rules (or AI model) created to the ALP system 210 to enable theALP system 210 to trigger the playing of media content by using therules (or AI model) in a rule engine. The rules (or AI model) may definerequirements (e.g. age group, gender, weather, etc.) for specific mediacontent to be played. The term “rule” may be a set of principles whichenables systems to make decisions based on given input. The input andaction performed may depend upon the system in which rules (or the AImodel) are stored.

In an embodiment, the ALP cloud 310 may update the rules (or AI model)and/or create new rules (or AI model) and send the updated and/or newrules (or AI model) to the ALP system 210 to update the set of rules (orAI model) stored in the ALP system 210. In an embodiment, the ALP cloud310 may create the rules (or AI model) based on data received by the ALPcloud 310. Such data may include data received from the local system120, such as from ALP system 210, and data received external from thelocal system 120. In an embodiment, the rules (or AI model) sent to theALP system 310 may be created or updated using machine learningtechniques performed by the ALP cloud 310.

In an embodiment, the ALP cloud 310 may create new media content using,for example, a set of rules or AI model stored in the ALP cloud 310. TheALP cloud 310 may send the media content created to the CMS 240 via theCMS cloud 340 to be stored by the CMS 240. Alternatively, the ALP cloud310 may send the media content created to the CMS 240 directly to bestored by the CMS 240.

With reference to FIG. 3 , the ALP cloud 310 is described in furtherdetail below. The ALP cloud 310 may comprise at least one processor andmemory comprising computer programming code configured to cause the atleast one processor to perform the functions of the ALP cloud 310.

FIG. 3 is a diagram illustrating example functions of the ALP cloud 310performable by at least one processor of the ALP cloud 310 with memoryof the ALP cloud 310. The memory may include computer program codeconfigured to cause the at least one processor to perform the functions.As illustrated in FIG. 3 , the ALP cloud 310 may provide modules of adata ingestion layer 312, a marketing engine: (ME) module 315, datastore 320, and customer analytics module (CAM) 325. The ME module 315may include functions of content stitching 316, content matching 317,CMS/CRM interface 318, and billing 319. The data store may be memorythat includes a rule engine or AT model 322, and includes a digitalasset management (DAM) storage 324. The CAM 325 may include a CAM AImodule 327 and a CAM BI module 329.

The data ingestion layer 312 may receive data 395 from different datasources (e.g. the local system 120 and external sources) as well ascleanse the data 395.

The CAM 325 may function as the analytics components of the remotesystem 130. The CAM 325 may encompass both in-store reporting and a bigdata application. Big data describes data sets that are large (bothstructured and unstructured) and complex, requiring advancedtechnologies to store, manage, analyze, and visualize information ofvalue. In an embodiment, the CAM 325 may function to (1) enableretailers to measure and monitor store performance, understand consumerbehavior, and implement beneficial actions; (2) perform analyticprocessing to optimize media content delivery to digital devices (e.g.displays 280) and measure impact, and (3) provide analytic support forthe ME module 315. In an embodiment, the CAM 325 may import, manage, andstore retailer and third-party data (both structured and unstructured).Various reporting, analytic models, and AI may process and analyze datato produce information and knowledge that enables decisions and actionsto be undertaken to enhance the ability of retailers to understand theneeds of their customers, predict their wants and demands, deliverrelevant messages, measure and monitor impact, and otherwise aid inoptimizing their use of resources.

In an embodiment, the CAM 325 may perform analysis using the rule engineor M model 322, and perform functions of data mining and reporting. Inan embodiment, by using the rule engine or AI model 322, the CAM 325 mayprovide information of value by a cycle of: converting data toinformation, to knowledge, to decisions, and then into actions thatenable retailers to understand the needs of their customers, predicttheir wants and demands, and optimize their use of resources.Accordingly, the CAM 325 may accomplish the challenging task of managingand gaining insights from data, thereby securing a competitiveadvantage.

In an embodiment, the CAM AI 327 may perform data analytics on real timedata for media content creation and/or creating and updating an M modelor rules to be used by the ALP system 210, by applying data 395 ingestedby the data ingestion layer 312 to the rule engine or AI model 322, TheCAM AI 327 may interface with data ingestion layer 312 to receive realtime data (hot path). The CAM 325 may also have an interface with the MEmodule 315. The CAM AI 327 may generate media content tags by applyingthe information from the data ingestion layer 312 to the rule engine orAI model 322, and provide these tags to the ME module 315 for furtherprocessing. For example, the tags may be applied to the ME module 315 toperform media content optimization by creating and selecting relevantmedia content for display. In an embodiment, the CAM AI 327 may createthe tags as recommendations for creation of targeted media content toenhance in-store customer experience and provide promotionaleffectiveness that leads to more dollar sales per customer. In anembodiment, functions of the CAM 325 may be visualized and controlledvia a CAM GUI 384 of the GUIs 380 that may be displayed on userterminal(s) 140 by the remote system 130.

The CAM BI 329 may generate various types of reports. Each of thesereport may be used to provide insights to individuals such as, forexample, store retailers and marketers. The reports may be implementedwith a retailer dashboard 382 and/or CAM GUI 384 of GUIs 380 that may bedisplayed on user terminal(s) 140 by the remote system 130. The reportsmay include reporting on, for example, store traffic, customerwait-time, store: conversion rates, store productivity rates, andvariances thereof. Additionally or alternatively, the reports mayinclude reporting on, for example, customer insights (e.g. viewinganalytics information) about customers in-store, zone traffic, inventoryand operational enhancements, impression counts, opportunity to see(OTS) counts, media content viewer rate, media content efficiency rate,media content conversion rate, the time people spent in certain areascovered by sensors, and information concerning relationships betweenradio frequency identification (RFID) and display messages.

Store conversion refers to, for example, the number of individuals (e.g.customers) present in a particular store. Store productivity refers to,for example, revenue divided by a number of individuals present in aparticular store. The reporting on demographics information may includethe demographics information for the people who have watched a triggeredmedia content for at least a predetermined time frame. The impressioncounts may be counts on many times people watched a triggered mediacontent for at least a predetermined time period. The OTS count may be acount of people who were determined to have had an opportunity to seemedia content on a display 280. For example, a count of people in frontof display 280 that have both watched and not watched media content onthe display 280.

The operational enhancements refer to, for example, detectingslow-moving inventory and detecting inventory approaching the end of itsuseful life. In an embodiment, the rule engine or AI model 322 may beused by the CAM AI 327 or the CAM BI 329 so as to determine and outputoperational enhancements based on the data 395 ingested by the dataingestion layer 312 and the ALP cloud 310, and the CAM BI 329 may causethe operational enhancements to be reported on one of the GUIs 380. Forexample, the ALP cloud 310 may recommend a specified inventory orpromotion as an operational enhancement. Accordingly, some embodimentsof the present disclosure may solve the problems retailers face inactively monitoring and promoting short-lived (like milk) andslow-moving product to maximize margins. Additionally, some embodimentsof the present disclosure may avoid on-demand inventory not beingmanaged well which may avoid a shortfall of inventory during peak-saletime periods, and may avoid poor inventory forecasting which may avoidreduced customer satisfaction resulting in downfall in the sale ofproducts. In an embodiment, the CAM AI 327 or the CAM BI 329 may run therule engine or AI model 322 on any periodical basis or any other basisto provide operational enhancements and other analytics. For example,the rule engine or AI model 322 may be run on a nightly or weekly basis.

The ME module 315 may store assets and static messages in the DAMstorage 324. The assets may include audio, video, still photography,logo graphics, animations, etc., and similar elements. In an embodiment,the assets may be used as components to create media content that isused as finished advertising spots. The ME module 315 may create newmedia content by, for example, auto-generating them with the assets andstatic messages stored in the DAM storage 325, based on analyticalinsights from data ingested by the ALP cloud 310 and instructions. Forexample, the ME module 315 may auto-generate the new media content basedon the media content tags, outputted by the rule set or the AI model 322of the CAM AI 327, being used as recommendation. In an embodiment, afterreceiving a media content tag, the ME module 315 may, by using thecontent matching 317 function, determine whether media content alreadyexists in the ALP cloud 310, the CMS system 240, and/or the CMS cloud340 that matches the recommendation signaled by the media content tagand, if there is no matching media content, create new media content perthe recommendation.

The creation of media content may be performed using the functions ofcontent stitching 316 and content matching 317. Content stitching 316may include combining different assets and/or static messages into asingle new media content. Content matching 317 may include matchinganalytic input and guidance issued by the CAM 325 to determine the newmedia content to be created and/or the media content to played by thedisplays 280. Functions of the ME module 315 (e.g. media contentcreation) may be visualized and controlled via the ME GUI 386 of theGUIs 380 that may be displayed on a user terminal(s) 140 by the remotesystem 130. For example, a user may create and manage media contentmanually with the ME module 315, via the ME GUI 386.

The ME module 315 may deliver the new media content to the CMS 240.Alternatively, if there is matching media content, the ME module 315 maynot create new media content and may send the matching media content tothe CMS 240 if the CMS 240 already does not have the matching mediacontent.

With reference to FIGS. 2 and 4 , components of the local system 120(e.g. the ALP system 210 and/or the CMS 240) may be configured toperform any number of the following example functions in embodiments:(1) receive data from devices 290 that may include sensors and POSdevices; (2) send the data from the devices 290 to the ALP cloud 310;(3) receive and store a rule set or AI model from the ALP cloud 310; (4)receive the data 295 from the external sources via the ALP cloud 310;(5) receive CMS playlist data from the ALP cloud 310 or the CMS cloud340; (6) apply the data from the sensors, the data from the POS devices,the data 295 from the external sources, and the CMS playlist data to therule set or the AI model received and stored; (7) select media contentfrom the plurality of media content and a display from the displays 280,based on an output of the rule set or the AI model received and stored;and (8) output a trigger to play the media content selected on thedisplay of the displays 280 selected.

For example, in an embodiment, the ALP system 210 determines what mediacontent stored in the CMS 240 is to be played on one or more of thedisplays 280, wherein the determination is based on the ALP system 210obtaining data and applying the data to rules 398 or an AI model storedin the ALP system 210.

With reference to FIGS. 2 and 4 , the ALP system 210 may comprise an ALPappliance 220 and memory 232. The ALP appliance 220 may include at leastone processor and memory. In an embodiment, the ALP appliance 220 maydownload the rules 398 or the AI model from the ALP cloud 310, andfurther receive data from the devices 290, which may be stored in memory232.

The devices 290 may include, for example, one or more cameras 260 thatsend video data (e.g. camera images) to the ALP system 210 or thedetection module 250. The devices 290 may also include, for example,mobile sensors, RFID, and beacons that provide sensor data to the ALPsystem 210 that the ALP system 210 may send to the ALP cloud 310 and/orapply to the stored rules 398 or the AI model. Such sensor data may alsobe received and used by the detection module 250 to perform itsdetection functions. Mobile sensors may be, for example, devices thatdetect MAC addresses of mobile devices in a detection area to count thenumber of devices as a traffic count. Beacons may be, for example, aBluetooth Low Energy (BLE) device that broadcasts a signal which can beidentified by other BLE devices in its vicinity. Beacons can be deployedindividually or as a network. The devices 290 may also include POSdevices which provide POS data to the ALP system 210 that the ALP system210 may send to the ALP cloud 310 and/or apply to the stored rules 398or the AI model.

As illustrated in FIG. 4 , one or more of the cameras 260 of the devices290 may be positioned nearby or integrated with one or more of thedisplays 280 to capture images of individuals 270 that may be viewingthe one or more of the displays 280. Some of the cameras 260 may also bepositioned away from the displays 280 at, for example, positions otherthan viewing positions of the displays 280. Each of the areas that aredetected by the sensors (e.g. cameras 260), of the devices 290, may be apart of a respective specified zone 115 of a location 110, wherein thelocation 110 may have only one zone 115 or a plurality of zones 115. Inan embodiments, one or more of the sensors may be directed to a singlezone 115 of a location 110. In embodiments, some of the zones 115 may bevisibility zones in which individuals 270 may view a display 280, andsome of the zones 115 may be audibility zones in which individuals 270may hear media content being played. In embodiments, one or moredisplays 280 may be inside or outside the zones 115. According toembodiments, each local system 120 may perform detection in a pluralityof the zones 115 of a respective location 110, wherein any number of thezones 115 may be viewing positions of the displays 280 of the localsystem 120.

The ALP system 210, or a device communicating therewith, may process thevideo data to obtain demographics data (e.g. age group, gender, etc.) ofone or more individuals 270 that is included in the video data. Forexample, the detection module 250 may process the video data to obtainthe demographics data. The detection module 250 may also process thevideo data to obtain, for example, crowd count and dwell time of storetraffic, orientation or line of sight of faces of the individuals 270.In an embodiment, the detection module may generate analytics datacontaining at least a date. The detection module 250 may be implementedby at least one processor with memory external to the ALP system 210, asillustrated in FIG. 4 , or implemented by the ALP system 210 asillustrated in FIG. 5 . The detection module 250 may include facialdetection or recognition software. In order to maintain customeranonymity, the detection module 250 may perform detection functions,without recognition functions, to obtain anonymous video analytics.Demographics data refers to the characteristics that define who a targetaudience is. For example, characteristics such as age, gender,ethnicity, cultural background, religious or political affiliations,economic status, family background, and group memberships may be thedemographics data obtained. Dwell time refers to a measurement of howlong individuals are present in front of a display 280.

The ALP system 210 may apply the demographics data to the stored rules398 or AI model to determine which content stored in the CMS 240 is tobe played on one more of the displays 280. In some embodiments, the ALPsystem 210 may also download data 295 from the ALP cloud 310 and applyit to the stored rules 398 or the stored AI model. The data 295downloaded may include, for example, event data, weather data, and POSdata. Event data may be, for example, data about past and/or futurelocal events, sports games (e.g. the results of the games), etc.

After reaching a determination of which content is to be played, the ALPsystem 210 may send a signal to the CMS 240 to play the content on oneor more of the displays 280. The signal may be, for example, aninstruction that triggers one or more specific media content of themedia content to play.

In some embodiments, the ALP system 210 may also upload data to the ALPcloud 310 for analysis, reporting, rule set or AI model updating, andother uses by the ALP cloud 310. The data uploaded may include all orsome of the information obtained by the detection module 250, all orsome of the other information obtained by the ALP appliance 220.

FIG. 5 illustrates example functions of the ALP appliance 220performable by at least one processor of the ALP appliance 220 withmemory (e.g. memory 232) of the ALP system 210. The memory may includecomputer program code that is configured to cause the at least oneprocessor to perform the functions. As illustrated in FIG. 5 , the ALPappliance 220 may provide modules of a detection module 250, edgecommunication module 221, rule engine/AI model 222, rule sync 223, datasource plugins 224, CMS interface 225, edge device management service226, edge device plugins 227, remote provision 228, ZMQ and Redis 229,and Internet of Things (IoT) edge client 230.

As illustrated in FIG. 5 , the detection module 250 may be implementedwith the ALP appliance 220 rather than as a separate component asillustrated in FIG. 4 .

The edge communication module 221 may be a module for communication withthe remote system 130, including the ALP cloud 310 and the CMS cloud340. For example, the ALP appliance 220 may retrieve new or updatedrules or a new or updated AI model using the edge communication module221. The ALP appliance may also upload data to the ALP cloud 310 usingthe edge communication module 221.

The ALP appliance 220 may implement the new rules/AI model with the ruleengine/AI model 222 used to determine content to be played, and mayupdate the rule engine/AI model 222 by synchronizing the rule engine/AImodel 222 with new or updated rules/AI model received by the edgecommunication module 221. In an embodiment, the rule engine/AI model 222may be ran with a limited set of data including, for example, a limitedset of demographics data and a limited set of external data. It shouldbe appreciated that the ALP cloud 310 of the remote system 130 mayreceive data from multiple local systems 120 and also receive externaldata from outside the system. Accordingly, unlike some embodiments ofthe ALP cloud 310 and its rule engine or AI model 322, the ALP appliance220 may not function as a big data analytics solution in someembodiments.

The data source plugins 224 and the edge device plugins 227 may beplugins for retrieving data from devices 290 including, for example,sensors, beacons, and RFID. The CMS interface 225 may interface the ALPappliance 220 with the CMS 240.

The ALP appliance 220 may include the edge device management service 6,edge device plugins 227, and remote provision 228 to accomplish thefunction of device management. For example, the edge device managementservice 226 may be used to update the other functions of the ALPappliance 220 including, for example, data source plugins 224 and therule engine/AI model 222. Also, the remote provision 228 may be used forremote login to the ALP appliance 220 for troubleshooting and managementof the ALP appliance 220.

The ZMQ and Redis cache may be used by the ALP appliance 220 to ingestand store data from different sources (e.g. the ALP cloud 310, devices290, and detection module 250) for further processing.

The IoT edge client 230 may function as a machine to machine (M2M)client and as a pipeline for sending data, including detection moduledata and data from devices 290 to the ALP cloud 310.

With reference to FIG. 2 , the CMS 240 may be a system that downloadsmedia content from the ALP cloud 310 and/or the CMS cloud 340, storesthe media content in memory of the CMS 240, and causes at least onedisplay of the displays 280 to play at least one media content of themedia content, based on the signal from the CMS 240. The CMS 240 maycomprise at least one processor and memory comprising computerprogramming code configured to cause the at least one processor toperform the functions of the CMS 240.

In an embodiment, the CMS 240 may send a proof of delivery (PoD)notification to the ALP cloud 310 indicating that the CMS 240 hasreceived the content from the ALP cloud 310 and/or the CMS cloud 340.The CMS 240 may send the PoD notification directly to the ALP cloud 310or to the ALP cloud 310 via the CMS cloud 340. In an embodiment, the CMS240 may send the PoD notification to the ALP system 210, and the ALPsystem 210 may send the PoD notification to the ALP cloud 310. In anembodiment, the CMS 240 may send information to the ALP system 210 thatindicates a list of the media content stored in the memory of CMS 240.The frequency or timing when the CMS 240 sends such information to theALP system 210 may be set at the CMS 240, and such sending ofinformation can be used as an inventory check of the CMS 240. In anembodiment, the CMS 240 may send the information once a day. In anembodiment, the CMS 240 may send a proof of play (PoP) notification toone or more of the ALP system 210, the ALP cloud 310, and the CMS cloud340, the PoP notification indicating that media content has been playedon at least one of the displays 280. For example, the PoP may be areporting mechanism in which logs are used to show that media content(e.g. an advertisement) actually played back on one or more of thedisplays 280. In an embodiment, the PoP notification may include atleast a date and time of play of media content, and an ID of the mediacontent played.

FIG. 6 illustrates an example embodiment of the CMS 240, wherein the CMS240 comprises at least one CMS player 242 and a content database 246.The CMS player 242 may include at least one processor and memorycomprising computer program code configured to cause the at least oneprocessor to perform the functions of the CMS 240. The content database246 may be memory that includes the media content that the CMS player242 may cause to play on one or more of the displays 280.

Details of use and generation of the AI models and rules of the presentdisclosure are described below.

The AI models of the present disclosure may be built over anunderstanding of the relationships between various data points, asdetermined by analytical processes. AI models of the present disclosuremay be built and updated using machine learning processes. For example,in an embodiment, the ALP cloud 310 may configured to run machinelearning processes to build and update an AI model used by the ALP cloud310 and an AI model or rule set used by the ALP system 210. The machinelearning processes may use supervised or un-supervised learningtechniques based on the kind of data inputted to the AI models.

As illustrated in FIG. 7 , the machine learning process flow may includea plan segment 910, an explore segment 920, a build segment 930, and anevaluate segment 940. The plan segment 910 may accomplish identifyingthe input data and framing the problem-solving approach. For example,the plan segment 910 may include a gather data step 912 and a clean datastep 914. The explore segment 920 may accomplish assessing datastructure and creating hypotheses, sample data, checking the statisticalproperties of the sample, and performing statistical tests ofhypotheses. For example, the explore segment 920 may include an initialdata analysis step 922 and a detailed data exploration step 924. Thebuild segment 930 may accomplish understanding technicalities of eachalgorithm and how it works on a particular set of data, and ensuringthat the model satisfies findings in the initial data analysis step 922.For example, the build segment 930 may include a build model step 932and a build data product step 934. The evaluate segment 940 mayaccomplish applying methods to validate the created model by, forexample, evaluating the models ability to perform and tuning the model.For example, the evaluate segment 940 may include a model evaluationstep 942 and a model tuning step 944. In an initial phase of the modelevaluation step 942, the AI model may target to maximize the lift withrespect to, for example, impression, dwell time, conversion rate, andproductivity. At further phases of the model evaluation step 942, the AImodel may target to maximize the lift for other aspects including, forexample, impression quality, opportunity to see (OTS), SNS review, andengagement level.

As illustrated in FIG. 9 , the process may loop back to the gather datastep 912 from the initial data analysis step 922, the process may lookback to the initial data analysis step 922 from the build model step932, the process may loop back to the build model step 932 from themodel evaluation step 942, and the process may look back to the gatherdata step 912 from the model evaluation step 942.

AI models and rule sets of the present disclosure may be configured toaccept various inputs and to output information. For example, AI modelsand rules sets of the present disclosure may accepts inputs such asdemographics information, store traffic information, crowd countinformation, wait time, weather information, point of sales (POS)information, catalog information, inventory information, CRMinformation, event information, display location information, storeinformation, retailer digital data, proof of play or proof of deliveryinformation, media content tags, sensor data, promotion data.

Demographics data refers to the characteristic of individuals 270 thatdefine who a target audience is. For example, characteristics such asage, gender, ethnicity, cultural background, religious or politicalaffiliations, economic status, family background, and group membershipsmay the demographics data obtained. In embodiments, the detection 250module may obtain the demographics data. The demographics data may beapplied to the rules or AI model to generate analytical reports based ondemographics of people visiting a location 110, and to generate ortrigger a targeted media content based on the demographics and otherrelated details.

Store traffic information may be, for example, information that comesfrom a camera 260 placed at an entrance of a location 110 concerning theamount of people entering the location 110. Crowd count information maybe, for example, a count of people in a specified zone 115 of a location110 (e.g. in front of a display 280). Store traffic information andcrowd count information may be applied to the rules or AI model togenerate analytical reports based on demographics of people visiting alocation 110, and to generate or trigger a targeted media content basedon the demographics and other related details.

Wait time may be, for example, data about how long individuals 270 havebeen waiting in a queue of any zone 115 of a location 110. Wait timedata may be applied to the rules or AI model to generate analyticalreports based on demographics of people visiting a location 110, togenerate or trigger a targeted media content based on the demographicsand other related details, generate an alert to staff to check forcustomer needs based on customer wait time crossing a threshold, andcreating and displaying a target media content with offers and discountsbased on high wait time and relevant static.

Weather information may include, for example, weather data based on zipcode. Weather information may be applied to the rules or AI model forpredictive analysis and for dynamic media content creation or triggeringbased on analytics of weather data along with other data.

POS information may include, for example, a retailer's data fortransactions happening over a POS machine. POS information may include,for example, customer purchase history based on transaction data,location of purchase, items purchased and corresponding quantity,customer purchasing habits, retail store sales information, storeconversion rate. POS information may be applied to the or AI model togenerate analytical reports based on demographics of people visiting alocation 110, and to generate or trigger a targeted media content basedon the demographics and other related details.

Catalog information may include, for example, a retailer's productcatalog data, that includes details of products and their pricinginformation. Campaign data may also be provided with the product catalogdata, wherein the campaign data is data for special events or a specifictime of the year. Inventory information may include, for example,inventory data of a retailer's location 110 and supply chain data.Catalog information may be applied to the rules or AI model to create ortrigger media content based on product offerings in the cataloginformation, wherein offer, discount, up selling, and cross-sellingmessages may be included based on the analytics and the product catalog.The campaign information may be applied to the rules or AI model tocreate or trigger media content based on a campaign ran by the retailer.

CRM information may include, for example, customer related data such ascustomer location and loyalty information. Such information may comefrom, for example, a retailer's CRM or loyalty program data. CRMinformation may be applied to the rules or AI model in analytics basedon customer demographics like age and gender, customer locationinformation (e.g. zip code) that provides information about purchasinghabits about people living in certain localities, and customer purchasehistory and preferences based on, for example, a loyalty program.

Event information may be, for example, data about past and/or futurelocal events, sports games (e.g. the results of the games), etc. Eventinformation may be applied to the rules or AI model to perform thepredictive analysis based on past events, to create reports based onevent data matched with sales, weather, location, etc., and to createand trigger media content based on the events.

Display location information may be, for example, information about aspecific location of a display 280 within a store. Display locationinformation may be applied to the rules or AI model to generateanalytical reports based on demographics of people visiting a location110, and to generate or trigger a targeted media content based on thedemographics and other related details.

Store information may be, for example, information about a retailer'slocation 110 (e.g. store). For example, store information may includedata of (1) location of the store, along with purchasing habits ofpeople in that locality; (2) weather conditions around the storelocation; (3) Events happening around the store location; and (4)distance from other competitive stores. The store information may beapplied to the rules or AI model to perform analytics based on the storedata, and corresponding sales, weather, events, store traffic, and otherdata. As well as for creating and trigger media contents based on thedata.

Retailer digital data may be, for example, data stored by a location 110concerning a specific customer. For example, the data may includespecific themes, images, videos, etc. for each specific customer inwhich data is provided. The retailer digital data may be applied to therules or AI model to create and trigger messages utilizing the specificthemes, images, videos, etc.

Sensor data may include, for example, RFID and beacon data. RFID datamay include information relating to tracking products in a store thatare tagged with RFID tags. Beacon data may include information relatedto location of customers maneuvering inside a store. Promotion dataincludes, for example, information about promotions applied on productswithin the location 110. The sensor data may be applied to the rules orAI model to generate analytical reports based on demographics of peoplevisiting a location 110, and to generate or trigger a targeted mediacontent based on the demographics and other related details.Additionally, the beacon data may be applied to the rules or AI model tobuild relation between a customer's in-store location, with othercustomer related data coming from other input sources (e.g. CRM,detection module 250, RFID, and locations of displays 280).

The AI models and rules sets of the present disclosure may output anytype of information that may be used for selecting media content todisplay or creating media content, including the media content tags. Inan embodiment, the media content tags may be media content creationrecommendations and message creation recommendations.

The media content tags may include, for example, meta-tags that areinformation associated with an asset n the form of simple keywords. Themeta-tags may be a subset of metadata and may be used for the purposesof denoting the subject matter of the asset. In an embodiment of thepresent disclosure, a media content tag may include, a probabilityfactor, media content meta-tag, a product stock keeping unit (SKU)information, a product meta-tag, age information, gender information,weather information, and event ID information. The media contentmeta-tag may be a unique ID that identifies media content. The productmeta-tag may be a unique tuple that identifies a product including, forexample, the department, classification, and category of the product.The age information may be an age of a customer for whom the mediacontent is targeted. The gender information may be a gender of acustomer for whom the media content is targeted. The weather informationmay weather information for which the media content is targeted. Theevent information may be information for which the media content istargeted. The media content tags generated may be used by the ALP cloud310 to generate optimized media content and trigger the playing of mediacontent, and by the ALP system 210 to trigger the playing of mediacontent.

An example of a media content tag for media content creation is providedbelow.

{  <Hit Probability> 90%  <Product SKU>  <Asset Meta-tag> {Fashion,Coat, North Face}  <age> 25-35  <gender> Female  <DS ID> DS101 }, { <Hit Probability> 50%  <Product SKU>  < Asset Meta-tag> {Fashion,Shoes, Adidas}  <age> 25-35  <gender> Female  <DS ID> DS101 }

Another example of a media content tag for media content creation,generated using different input data, is provided below.

{  <Hit Probability> 70%  <Product SKU>  < Asset Meta-tag> {Toys &Games, Video Games, PS4}  <age> 15-25  <gender> Male  <DS ID> DS120 }, { <Hit Probability> 50%  <Product SKU>  < Asset Meta-tag> {Fashion, Polo,USPA}  <age> 15-25  <gender> Male  <DS ID> DS120 }, {  <Hit Probability>30%  <Product SKU>  < Asset Meta-tag> {Fashion, Shades, Ray Ban}  <age>15-25  <gender> Male  <DS ID> DS120 }

An example of a media content tag for media content selection isprovided below.

{  <Hit Probability> 90%  <Product SKU>  <Message Meta-tag> MSG-2061 (itcould be {Fashion, Coat, North  Face})  <age> 25-35  <gender> Female <DS ID> DS101 }, {  <Hit Probability> 50%  <Product SKU>  <ProductMeta-tag> MSG-2761 (it could be {Fashion, Shoes, Adidas})  <age> 25-35 <gender> Female  <DS ID> DS101 }

Another example of a media content tag for media content selection,generated using different input data, is provided below.

{  <Hit Probability> 70%  <Product SKU>  <Product Meta-tag> MSG-101{Toys & Games, Video Games, PS4}  <age> 15-25  <gender> Male  <DS ID>DS120 }, {  <Hit Probability> 50%  <Product SKU>  <Product Meta-tag>MSG-161 ({Fashion, Polo, USPA})  <age> 15-25  <gender> Male  <DS ID>DS120 }, {  <Hit Probability> 30%  <Product SKU>  <Product Meta-tag>MSG-186 ({Fashion, Shades, Ray Ban})  <age> 15-25  <gender> Male  <DSID> DS120 }

In an embodiment, the outputted media content tags may also be theinputs of the AI models. For example, the media content tags may beoutput by the CAM AI 327, illustrated in FIG. 3 , for retraining the AImodel(s) used by the CAM AI 327 and the AI model(s) to be used by theALP system 210. In an embodiment, the AI model(s) may be retrained toachieve a specified level of lift, which may be a measure of performanceof the AI model. The term “lift” refers to a ratio of target responsedivided by average response.

Embodiments of the present disclosure may accomplish the methodillustrated in FIG. 8 . For example, referring to the embodimentillustrated in FIG. 2 , the ALP cloud 310 may create one or more mediacontent according to manual instructions of a user using a user terminal140, or according to rules or an AI model stored in the ALP cloud 310,and deliver the media content to the CMS 240 via the CMS cloud 340 (step410). Following, the CMS 240 may send a proof of delivery notificationto the ALP cloud (step 420). The CMS 240 may also send a list of themedia content stored in the CMS 240 to the ALP system 210 (step 430).The ALP cloud 310 may deliver a rule(s) or an AI model to the ALP system210 that may be stored and used by the ALP cloud 310 to trigger certainmedia content to be played based on inputs to the rule(s) or the AImodel.

In an embodiment, step 440 may be executed after step 420 to avoidtriggering, before the CMS 240 receives the media content, the attemptedplaying of the media content based on the delivered rule(s) or AI model.In such a case, step 430 may be omitted. In an embodiment, the ALPsystem 210 can send an instruction to the CMS 240 as a trigger todisplay a specific media content on one or more displays 280 only if thespecific media content is included in the list of media content receivedby the ALP system 210 in step 430. For example, the ALP system 210 maydetermine a media content to play based in part on the list of mediacontent received. In such a case, step 420 may be omitted.

Embodiments of the present disclosure may accomplish the methodillustrated in FIG. 9 . For example, referring to FIGS. 2 and 5 , one ormore cameras 260 may capture video, including images of at least one ofthe individuals 270 (step 510). Following, the detection module 250 mayprocess the images from the video to acquire demographic data of the atleast one of the individuals 270 (step 520). Additionally, the ALPsystem 210 may receive external data 295 fro m the ALP cloud 310 andother data from devices 290 (e.g. mobile sensor data, RFID data, beacondata) (step 530), and apply such data to rules or an AI model stored inthe ALP system 210 (step 540). In an embodiment, the ALP system 210 mayapply at least the demographic data acquired to the rule engine/AI model222, the rule engine containing the rules. The ALP system 210 maytrigger a specified media content to play based on an output of therules or the AI model (step 550). For example, the ALP system 210 maysend an instruction to the CMS 240 as a trigger to display the specifiedmedia content on one or more of the displays 280 connected to the CMS240.

FIGS. 10A-E illustrate diagrams for demonstrating a sequence ofinteractions between the ALP system 210 and the CMS 240 of anembodiment, for triggering the playing of media content when the CMS 240is currently playing media content in accordance with a CMS playlist600.

In an embodiment, the CMS 240 may be configured to create and schedule aCMS playlist 600 to be played by one or more of the displays, and managethe media content list within the CMS playlist 600. The ALP system 210may be configured to send a trigger to the CMS 240 for selecting a mediacontent that resides within or outside the CMS playlist 600 for playing.

Based on information received from various data sources, the ALP system210 selects the appropriate media content to be played and sends atrigger every predetermined time interval (e.g. every 1 second) to theCMS 240 to play a media content. The CMS 240 may ignore the triggersuntil a predetermined time period before a currently played mediacontent ends, wherein the predetermined time period can be changed basedon performance of the system. For example, with reference to FIG. 10A,the CMS 240 may accept a trigger at (N−1)th second of a currently playedmedia content 610, wherein N is the length of the media content, andignore the triggers received before the (N−1)th second of the currentlyplayed media content 610.

Based on the trigger being accepted, the CMS 240 may play the mediacontent for which it has received the trigger. For example, withreference to FIG. 10B, the CMS 240 may play the media content 620 if thetrigger received at (N−1)th second of the media content 610 played inFIG. 10A is for media content 620.

In case the CMS 240 does not receive a trigger at (N−1) sec of acurrently played media content, the CMS 240 may accept the triggerreceived at a different predetermined time frame (e.g. (N−2) sec of acurrently played media content) and play the media content referenced bythe trigger, wherein such predetermined time period can be changed basedon performance of the system. However, if the CMS 240 does not receive atrigger at the different time frame (e.g. (N−2) sec of a currentlyplayed media content), the CMS 240 may cause a next media content in theCMS playlist 600 to be played based on rules. For example, the CMS 240may cause a lowest ordered media content of the CMS playlist 600 to playthat was not previously played, a lowest ordered media content of theCMS playlist 600 to play that was not previously automatically triggeredby the ALP system 210, or the next ordered media content of the CMSplaylist 600 relative to the presently played media content. Forexample, with reference to FIG. 10C, the CMS 240 may play media content630, the second media content in the CMS playlist 600, since mediacontent 630 is the earliest media content in the CMS playlist 600 thathas not been previously played. Additionally, with reference to FIG.10D, the CMS 240 may play media content 640, the third media content inthe CMS playlist 600, in a case where a trigger was not received at(N−1) sec and (N−2) sec of the played media content 630.

In further reference to FIG. 10D, based on the trigger not beingreceived at (N−1) sec of the played media content 640 but a triggerbeing received at (N−2) sec of the played media content 640, the CMS 240may play the media content for which it has received the trigger. Forexample, with reference to FIG. 10E, the CMS 240 may play the mediacontent 650 if the trigger received at (N−2)th second of the mediacontent 640 played in FIG. 10D is for media content 650.

In embodiments of the present disclosure, each of the ALP system 210,the CMS 240, and the cloud services (including the CAM 325, the MEmodule 315, and the CMS cloud 340) may be implemented by at least oneprocessor. For example, in an embodiment, the ALP system and the CMS 240may be implemented by the same or separate at least one first processor,and the CAM 325, ME module 315, and the CMS cloud 340 of the cloudservices may be implemented by the same or separate at least one secondprocessor.

Aspects of the present disclosure relating to the creation of new mediacontent and the triggering of playing of media content may achieve abenefit of enabling media content that is more in-line to specificcustomer segment needs to be created and played. Accordingly, someembodiments of the present disclosure may cause created and triggeredmedia content to be more engaging, thus potentially leading to moresales in stores. Moreover, by providing individuals with targetedproduct and service information in the form of media content, theindividuals engagement and loyalty may increase. Accordingly, someembodiments of the present disclosure result in the enhancement ofcustomer experience (e.g. impressions), thus leading to higher sales forretailers. Also, some embodiments of the present disclosure enable usersto understand who is in a store or aisle at a specific time, thetriggering of the relevant media content to be played, the users tounderstand the impact of media content on individuals, and theprediction of what media content will be most relevant to theindividuals.

The whole or part of the example embodiments disclosed above can bedescribed as, but not limited to, the following supplementary notes.

Supplemental Note 1

A device comprising:

-   -   a memory having program instructions stored therein; and    -   at least one processor configured to execute the program        instructions to control the device to:        -   connect via a wide area network to a remote system,        -   receive a rule set or an artificial intelligence (AI) model            from the remote system,        -   select media content from a plurality of media content based            on applying demographics data to the rule set or AI model,            and        -   cause, based on an output of the rule set or AI model, a            display, that is local to the device, to display the media            content.

Supplemental Note 2

The device of SUPPLEMENTAL NOTE 1 wherein

-   -   the at least one processor is further configured to obtain        customer demographics data as the demographic data based on        sensor data from at least one sensor, and apply the customer        demographics data to the rule set or AI model.

Supplemental Note 3

The device of SUPPLEMENTAL NOTE 1 or 2, wherein

-   -   the at least one processor is further configured to obtain data        from at least one from among a mobile sensor, a radio frequency        identification device, and a beacon.

Supplemental Note 4

A system comprising:

-   -   the device according to SUPPLEMENTAL NOTE 2; and    -   the at least one sensor, wherein    -   the at least one sensor includes a first camera and a second        camera,    -   the first camera is located in a vicinity of the display, and    -   the second camera is located in a position, away from the        display.

Supplemental Note 5

The device of SUPPLEMENTAL NOTE 2, wherein

-   -   the at least one processor is configured to send, to the remote        system, the customer demographics data.

Supplemental Note 6

The device of SUPPLEMENTAL NOTE 5, wherein

-   -   the at least one processor is configured to send, to the remote        system, only a portion of the customer demographics data that is        obtained by the at least one processor, such that an amount of        the customer demographics data sent to the remote system by the        at least one processor is less than a total amount of the        customer demographics data obtained by the at least one        processor.

Supplemental Note 7

The device of SUPPLEMENTAL NOTE 1 or 5, wherein

-   -   the at least one processor is further configured to receive,        from the remote system, external data including at least one        from among event data, weather data, and point of sale (POS)        data, and select the media content from the plurality of media        content based on applying the demographics data and the external        data to the rule set or AI model.

Supplemental Note 8

The device of SUPPLEMENTAL NOTE 1 or 5, wherein

-   -   the at least one processor is configured to receive, from the        remote system, an updated rule of the rule set, an updated rule        set, or an updated AI: model, that is updated based on the        customer demographics data sent to the remote system, and is        further configured to cause the media content from the plurality        of media content to be displayed on the display, based on the        updated rule, the updated rule set, or the updated AI model.

Supplemental Note 9

The device of SUPPLEMENTAL NOTE 1-8, wherein

-   -   the plurality of media content is stored in memory that is        external to the device, and    -   the at least one processor is configured to cause, based on the        output of the rule set or AI model, the display to display the        media content from the plurality of media content that is stored        in the memory that is external to the device.

Supplemental Note 10

A system comprising:

-   -   the device according to SUPPLEMENTAL NOTE 1, 4, 5, or 6; and    -   a content management system (CMS) connected by a local network        with the device, wherein    -   the device is configured to receive the rule set or AI model        from the remote system, select the media content from the        plurality of media content based on applying the demographic        data to the rule set or AI model, and send, based on the output        of the rule set or AI model, a signal to the CMS to display the        media content from the plurality of media content, and    -   the CMS comprises at least one processor and a memory, and is        configured to receive the plurality of media content from the        remote system, store the plurality of media content in the        memory of the CMS, and cause, based on the signal from the ALP        appliance, the display to display the media content.

Supplemental Note 11

The system of SUPPLEMENTAL NOTE 10, wherein

-   -   the CMS is configured to send a list of the plurality of media        content stored by the CMS to the device, and    -   the device is configured to send the signal to the CMS to        display the media content from the plurality of media content,        based on the list of the plurality of media content being        previously received by the device.

Supplemental Note 12

The system of SUPPLEMENTAL NOTE 10 or 11, wherein

-   -   the device is configured to send a plurality of signals,        including the signal, to the CMS to trigger the CMS to display        one or more of the plurality of media content on the display,        each of the plurality of signals sent at a different time, and    -   the CMS is configured to receive the plurality of signals, while        playing media content of a playlist on the display, and further        configured to:        -   ignore each of the plurality of signals received prior to a            first predetermined time of the media content of the            playlist played,        -   in a case where one of the plurality of signals is received            at a second predetermined time of the media content of the            playlist played, later than the first predetermined time,            play one of the plurality of media content based on the one            of the plurality of signals received,        -   in a case where none of the plurality of signals is received            at the second predetermined time and the one of the            plurality of signals is received at the first predetermined            time, play the one of the plurality of media content based            on the one of the plurality of signals received, and        -   in a case where none of the plurality of signals is received            at the first predetermined time and the second predetermined            time, play other media content of the playlist based on a            position of the other media content within the playlist.

Supplemental Note 13

A system comprising:

-   -   a device comprising at least one processor and a memory; and    -   a remote system configured to communicate with the device via a        wide area network, wherein    -   the remote system is configured to create a rule set or AI model        and send the rule set or AI model via the wide area network to        the device, the rule set or AI model configured for selecting        media content among a plurality of media content for displaying        on a display, that is local to the device, and    -   the device is configured to select the media content from the        plurality of media content based on applying demographics data        to the rule set or AI model, and cause, based on an output of        the rule set or AI model, the display to display the media        content.

Supplemental Note 14

The system of SUPPLEMENTAL NOTE 13, wherein

-   -   the remote system is further configured to receive at least a        portion of the demographics data obtained by the device, and        update the rule set or AI model sent to the device based on the        portion of demographics data received by the remote system from        the device.

Supplemental Note 15

The system of SUPPLEMENTAL NOTE 13 or 14, wherein

-   -   the remote system is further configured receive external data        including at least one from among event data, weather data, and        point of sale (POS) data, and send the external data to the        device, and    -   the device is configured to select the media content based on        applying the demographics data and the external data to the rule        set or AI model.

Supplemental Note 16

The system of SUPPLEMENTAL NOTE 13, 14, or 15, wherein

-   -   the remote system is further configured to create new media        content based on the portion of demographics data received by        the remote system from the device, and send the new media        content to a content management system (CMS), that is connected        locally with the device and the display, to be stored in memory        of the CMS device as a part of the plurality of media content.

Supplemental Note 17

The system of SUPPLEMENTAL NOTE 13, 14, 15, or 16, wherein

-   -   the remote system is further configured to create customer        insights for display on a user display, based on the portion of        demographics data received by the remote system from the device.

Supplemental Note 18

The system of SUPPLEMENTAL NOTE 13, 14, 15, 16, or 17, wherein

-   -   the remote system is configured to receive a proof of delivery        (PoD) notification from a content management system (CMS), that        is connected locally with the device and the display, and send        the rule set or AI model via the wide area network to the device        based on previously receiving the PoD notification.

Supplemental Note 19

A method performed by a device comprising at least one processor and amemory, the device connected via a wide area network to a remote system,the method comprising:

-   -   receiving a rule set or artificial intelligence (AI) model from        the remote system;    -   selecting media content from a plurality of media content based        on applying demographics data to the rule set or AI model; and    -   causing, based on an output of the rule set or AI model, a        display, that is local to the device, to display the media        content.

Supplemental Note 20

The method of SUPPLEMENTAL NOTE 19, further comprising:

-   -   obtaining the demographics data based on sensor data from at        least one sensor that is connected locally with the at least one        processor of the device.

Supplemental Note 21

The method of SUPPLEMENTAL NOTE 19 or 20, further comprising:

-   -   uploading, to the remote system, at least a portion of the        demographics data obtained by the device.

Supplemental Note 22

The method of SUPPLEMENTAL NOTE 19, 20, or 21, further comprising:

-   -   downloading, from the remote system, external data including at        least one from among event data, weather data, and point of sale        (POS) data, wherein    -   the selecting comprises selecting the media content from the        plurality of media content based on applying the demographics        data and the external data to the rule set or AI model. As used        herein, the term component is intended to be broadly construed        as hardware, firmware, or a combination of hardware and        software.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theforms explicitly described. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of embodiments of the present disclosure.

Even though combinations of features are recited in the claims and/ordisclosed in the specification, these combinations are not intended tolimit the disclosure of possible implementations. Many of the describedfeatures may be combined in ways not explicitly recited in the claimsand/or explicitly described in the above disclosure. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Also, as usedherein, the terms “has,” “have,” “having,” or the like are intended tobe open-ended terms. Further, the phrase “based on” is intended to mean“based, at least in part, on” unless explicitly stated otherwise. Theterm “or” as used herein is an inclusive “or”, and has a meaningequivalent to “and/or.”

What is claimed is:
 1. A local system comprising: at least one memoryhaving program instructions stored therein; and at least one processorconfigured to execute the program instructions to implement a device anda content management system (CMS) and control the local system to:connect, by the device, via a wide area network to a remote system,receive, by the device, a rule set or an artificial intelligence (AI)model from the remote system, select, by the device, first media contentbased on applying first demographics data to the rule set or AI model,cause, by the device and based on a first output of the rule set or AImodel, a first display, that is local to and separate from the device,to display the first media content, send, by the device and to the CMS,a plurality of signals each triggering the CMS to play on the firstdisplay, a respective one of a plurality of media content of a playlistincluding the first media content, each of the plurality of signals sentat a different time while the first media content of the playlist isplayed on the first display, ignore, by the CMS, each of the pluralityof signals received prior to a first predetermined time of the playedfirst media content of the playlist, in a case where one of theplurality of signals is received at a second predetermined time of theplayed first media content of the playlist, later than the firstpredetermined time, play, by the CMS and on the first display, one ofthe plurality of media content of the playlist, based on the one of theplurality of signals received, in a case where none of the plurality ofsignals is received at the second predetermined time and the one of theplurality of signals is received at the first predetermined time, play,by the CMS and on the first display, the one of the plurality of mediacontent of the playlist, based on the one of the plurality of signalsreceived, and in a case where none of the plurality of signals isreceived at the first predetermined time and the second predeterminedtime, play, by the CMS and on the first display, other media content ofthe playlist, based on a position of the other media content within theplaylist.
 2. The local system of claim 1, wherein the at least oneprocessor is further configured to obtain customer demographics data asthe first demographics data based on sensor data from at least onesensor, and apply the customer demographics data to the rule set or AImodel.
 3. The local system of claim 2, wherein the at least oneprocessor is further configured to obtain, by the device, data from atleast one from among a mobile sensor, a radio frequency identificationdevice, and a beacon.
 4. The local system of claim 2, furthercomprising: the at least one sensor, wherein the at least one sensorincludes a first camera and a second camera, the first camera is locatedin a vicinity of the first display, and the second camera is located ina position, away from the first display.
 5. The local system of claim 2,wherein the at least one processor is configured to send, by the deviceand to the remote system, the customer demographics data.
 6. The localsystem of claim 5, wherein the at least one processor is configured tosend, by the device and to the remote system, only a portion of thecustomer demographics data that is obtained by the at least oneprocessor, such that an amount of the customer demographics data sent tothe remote system by the at least one processor is less than a totalamount of the customer demographics data obtained by the at least oneprocessor.
 7. The local system of claim 5, wherein the at least oneprocessor is further configured to receive, by the device and from theremote system, external data including at least one from among eventdata, weather data, and point of sale (POS) data, and select the firstmedia content based on applying the first demographics data and theexternal data to the rule set or AI model.
 8. The local system of claim5, wherein the at least one processor is configured to receive, by thedevice and from the remote system, an updated rule of the rule set, anupdated rule set, or an updated AI model, that is updated based on thecustomer demographics data sent to the remote system, and is furtherconfigured to cause the first media content to be displayed on the firstdisplay, based on the updated rule, the updated rule set, or the updatedAI model.
 9. The local system of claim 1, wherein the plurality of mediacontent are stored in memory that is external to the device, and the atleast one processor is configured to cause, by the device and based onthe first output of the rule set or AI model, the first display todisplay the first media content that is stored in the memory that isexternal to the device.
 10. The local system of claim 1, wherein the CMSis connected by a local network with the device, wherein the device isconfigured to receive the rule set or AI model from the remote system,select the first media content based on applying the first demographicsdata to the rule set or AI model, and send, based on the first output ofthe rule set or AI model, a first signal to the CMS, the first signaltriggering the CMS to play the first media content on the first display,and the CMS is configured to receive the first media content from theremote system, store the first media content in memory of the CMS, andcause, based on the first signal from the device, the first display todisplay the first media content.
 11. The local system of claim 10,wherein the CMS is further configured to send to the device, theplaylist including the first media content stored by the CMS, and thedevice is configured to send the first signal, based on the playlistbeing previously received by the device.
 12. A system comprising: alocal system comprising at least one processor and at least one memory;and a remote system configured to communicate with the local system viaa wide area network, wherein the remote system is configured to create arule set or AI model and send the rule set or AI model via the wide areanetwork to a device of the local system, the rule set or AI modelconfigured for selecting at least one media content for displaying on atleast one display from among a plurality of displays, that are local toand separate from the device, and the local system is configured to:select, by the device, first media content based on applying firstdemographics data to the rule set or AI model, cause, by the device andbased on a first output of the rule set or AI model, a first displayfrom among the plurality of displays to display the first media content,send, by the device and to a content management system (CMS) of thelocal system, a plurality of signals each triggering the CMS to play onthe first display, a respective one of a plurality of media content of aplaylist including the first media content, each of the plurality ofsignals sent at a different time while the first media content of theplaylist is played on the first display, ignore, by the CMS, each of theplurality of signals received prior to a first predetermined time of theplayed first media content of the playlist, in a case where one of theplurality of signals is received at a second predetermined time of theplayed first media content of the playlist, later than the firstpredetermined time, play, by the CMS and on the first display, one ofthe plurality of media content of the playlist, based on the one of theplurality of signals received, in a case where none of the plurality ofsignals is received at the second predetermined time and the one of theplurality of signals is received at the first predetermined time, play,by the CMS and on the first display, the one of the plurality of mediacontent of the playlist, based on the one of the plurality of signalsreceived, and in a case where none of the plurality of signals isreceived at the first predetermined time and the second predeterminedtime, play, by the CMS and on the first display, other media content ofthe playlist, based on a position of the other media content within theplaylist.
 13. The system of claim 12, wherein the remote system isfurther configured to receive at least a portion of the firstdemographics data obtained by the device, and update the rule set or AImodel sent to the device based on the portion of the first demographicsdata received by the remote system from the device.
 14. The system ofclaim 13, wherein the remote system is further configured to receiveexternal data including at least one from among event data, weatherdata, and point of sale (POS) data, and send the external data to thedevice, and the device is configured to select the first media contentbased on applying the first demographics data and the external data tothe rule set or AI model.
 15. The system of claim 13, wherein the remotesystem is further configured to create new media content based on theportion of the first demographics data received by the remote systemfrom the device, and send the new media content to the CMS, that isconnected locally with the device and the first display, to be stored inmemory of the CMS as a part of the plurality of media content.
 16. Thesystem of claim 13, wherein the remote system is further configured tocreate customer insights for display on a user display, based on theportion of the first demographics data received by the remote systemfrom the device.
 17. The system of claim 12, wherein the remote systemis configured to receive a proof of delivery (PoD) notification from theCMS, that is connected locally with the device and the first display,and send the rule set or AI model via the wide area network to thedevice based on previously receiving the PoD notification.
 18. A methodperformed by a local system comprising at least one processor and atleast one memory, the local system connected via a wide area network toa remote system, the method comprising: receiving, by a device of thelocal system, a rule set or an artificial intelligence (AI) model fromthe remote system; selecting, by the device, first media content basedon applying first demographics data to the rule set or AI model;causing, by the device and based on a first output of the rule set or AImodel, a first display, that is local to and separate from the at leastone processor, to display the first media content; sending, by thedevice and to a content management system (CMS) of the local system, aplurality of signals each triggering the CMS to play on the firstdisplay, a respective one of a plurality of media content of a playlistincluding the first media content, each of the plurality of signals sentat a different time while the first media content of the playlist isplayed on the first display; ignoring, by the CMS, each of the pluralityof signals received prior to a first predetermined time of the playedfirst media content of the playlist; in a case where one of theplurality of signals is received at a second predetermined time of theplayed first media content of the playlist, later than the firstpredetermined time, playing, by the CMS and on the first display, one ofthe plurality of media content of the playlist, based on the one of theplurality of signals received; in a case where none of the plurality ofsignals is received at the second predetermined time and the one of theplurality of signals is received at the first predetermined time,playing, by the CMS and on the first display, the one of the pluralityof media content of the playlist, based on the one of the plurality ofsignals received; and in a case where none of the plurality of signalsis received at the first predetermined time and the second predeterminedtime, playing, by the CMS and on the first display, other media contentof the playlist, based on a position of the other media content withinthe playlist.
 19. The method of claim 18, further comprising: obtainingthe first demographics data based on sensor data from at least onesensor that is connected locally with the at least one processor of thelocal system.
 20. The method of claim 18, further comprising: uploading,to the remote system, at least a portion of the first demographics dataobtained by the local system.
 21. The method of claim 20, furthercomprising: downloading, from the remote system, external data includingat least one from among event data, weather data, and point of sale(POS) data, wherein the selecting comprises selecting the first mediacontent based on applying the first demographics data and the externaldata to the rule set or AI model.
 22. The device of claim 1, wherein thedevice is an edge device.
 23. The system of claim 12, wherein the deviceis an edge device.
 24. The method of claim 18, wherein the device is anedge device.
 25. A system comprising: a local system comprising: atleast one memory having program instructions stored therein; and atleast one processor configured to execute the program instructions toimplement a device and a content management system (CMS) and control thelocal system to: connect, by the device, via a wide area network to aremote system, receive, by the device and from the remote system, a ruleset, which comprises at least one rule, or an artificial intelligence(AI) model, wherein the at least one rule or the AI model is created inthe remote system and is associated with at least one media content,obtain, by the device, and based on first sensor data from at least onesensor, first customer demographics data that indicates an age group orgender, select, by the device, first media content based on a firstoutput of the rule set or the AI model to which the first customerdemographics data is applied as a first input, cause, by the device, afirst display, that is local to and separate from the device, to displaythe first media content that is selected, obtain, by the device andbased on second sensor data sensed by the at least one sensor while thefirst display is caused to display the first media content selected,analytic data that corresponds to viewing analytics information aboutcustomers that are viewing the first media content displayed on thefirst display, send, by the device and to the remote system, theanalytic data for updating the at least one rule, the rule set, or theAI model, send, by the device and to the CMS, a plurality of signalseach triggering the CMS to play on the first display, a respective oneof a plurality of media content of a playlist including the first mediacontent, each of the plurality of signals sent at a different time whilethe first media content of the playlist is played on the first display,ignore, by the CMS, each of the plurality of signals received prior to afirst predetermined time of the played first media content of theplaylist, in a case where one of the plurality of signals is received ata second predetermined time of the played first media content of theplaylist, later than the first predetermined time, play, by the CMS andon the first display, one of the plurality of media content of theplaylist, based on the one of the plurality of signals received, in acase where none of the plurality of signals is received at the secondpredetermined time and the one of the plurality of signals is receivedat the first predetermined time, play, by the CMS and on the firstdisplay, the one of the plurality of media content of the playlist,based on the one of the plurality of signals received, and in a casewhere none of the plurality of signals is received at the firstpredetermined time and the second predetermined time, play, by the CMSand on the first display, other media content of the playlist, based ona position of the other media content within the playlist.
 26. Thedevice of claim 25, wherein the device is an edge device.